Anomaly event detection using frequent patterns

ABSTRACT

A method is disclosed. The method includes: receiving, at a computing device, an event log including a plurality of events, where the plurality of events are derived from machine data generated by components of an information technology environment; determining a first score associated with a first granularity level by comparing a first event from the event log with a first plurality of frequent patterns generated for the first granularity level; determining a second score associated with a second granularity level by comparing the first event with a second plurality of frequent patterns generated for the second granularity level; determining an aggregate score for the first event based on the first score and the second score; comparing the aggregate score for the first event with an anomaly score threshold; and issuing an alert identifying the first event as an anomaly based on the aggregate score exceeding the anomaly score threshold.

BACKGROUND

Network systems are a set of interconnected devices that togetherprovide computing functionality. For example, networks may includestorage devices, end-user terminals, application servers, routers, andother devices. Security and proper resource management are concerns fornetworks of all sizes. Accordingly, one or more components associatedwith the network should quickly and efficiently detect an anomalousevent because such an event may correspond to a security threat to thenetwork (e.g., unauthorized access, attack, etc.) and/or an inefficientuse of network resources (e.g., excessive bandwidth consumption).

BRIEF DESCRIPTION OF DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system in which an embodiment may be implemented;

FIG. 8 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 9 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 10 illustrates a block diagram of an example classifier and datastore in which an embodiment may be implemented;

FIG. 11 illustrates an example binary encoding process in accordancewith disclosed embodiments;

FIG. 12 illustrates a block diagram of a scoring model and data storesin accordance with disclosed embodiments;

FIG. 13 illustrates a block diagram of a set of frequency patternsbelonging to a single granularity level in accordance with the disclosedembodiments;

FIG. 14 illustrates a block diagram of an anomaly detector and datastores in accordance with the disclosed embodiments;

FIG. 15 is a flow diagram that illustrates how to identify events thatare anomalies in accordance with disclosed embodiments;

FIG. 16 is a flow diagram that illustrates how to calculate alevel-specific score for an event in accordance with disclosedembodiments;

FIG. 17 is a flow diagram that illustrates how to process potentiallyanomalous events in in accordance with disclosed embodiments;

FIG. 18 is a flow diagram that illustrates how to cluster events byactivity type using binary encoding in accordance with disclosedembodiments;

FIG. 19 is a flow diagram that illustrates how to calculate a minimumsupport value for use in a frequent itemset mining algorithm inaccordance with disclosed embodiments.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. However, it will be apparent toone of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as by the use ofthe terms “before”, “after”, “single”, and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

Further, although the description includes a discussion of variousembodiments, the various disclosed embodiments may be combined invirtually any manner. All combinations are contemplated herein.

In general, embodiments are directed to an efficient technique fordetecting anomalous events. In one or more embodiments, multiple scoresare calculated for an event by comparing the features in the event withmultiple frequent patterns spanning one or more granularity levels. Anaggregate score for the event is calculated for the event based themultiple scores, and the aggregate score is used to determine whetherthe event is an anomaly (e.g., a potential security threat to thenetwork, a fault or failure of one or more hardware and/or softwarecomponents of the computing environment, inefficient use of resources).The frequent patterns are generated by executing one or more frequentitemset mining algorithms on historic events that have been classifiedas normal (i.e., not anomalies).

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

-   -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7. Field Extraction    -   2.8. Data Modelling    -   2.9. Acceleration Techniques        -   2.9.1. Aggregation Technique        -   2.9.2. Keyword Index        -   2.9.3. High Performance Analytics Store        -   2.9.4. Accelerating Report Generation    -   2.10. Security Features    -   2.11. Data Center Monitoring    -   2.12. Cloud-Based System Overview    -   2.13. Searching Externally Archived Data        -   2.13.1. ERP Process Features    -   2.14. IT Service Monitoring        3.0. Anomaly Event Detection        4.0. Hardware

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “Site-Based Search Affinity”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “Multi-SiteClustering”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an example process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “I” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a firewall 501 running on thenetwork processes traffic from outside the company and allows or deniestraffic. The firewall 501 creates a log entry 504 for the trafficprocessed. A router 502 running on the network routes traffic throughthe network. The router 502 creates a log entry 505 for the trafficprocessed. The systems 501 and 502 are disparate systems that do nothave a common logging format. The log data 504 and 505 are sent to theSPLUNK® ENTERPRISE system in different formats.

Using the log data received at one or more indexers 206 from thesystems, the vendor can uniquely obtain an insight into device behavior.The search head 210 allows the vendor's administrator to search the logdata from the systems that one or more indexers 206 are responsible forsearching, thereby obtaining correlated information, such as the deviceID. The device ID field value exists in the data gathered from thesystems, but the device ID field value may be located in different areasof the data given differences in the architecture of the systems—thereis a semantic relationship between the device ID field values generatedby the systems. The search head 210 requests event data from the one ormore indexers 206 to gather relevant event data from the systems. Itthen applies extraction rules to the event data in order to extractfield values that it can correlate. The search head may apply adifferent extraction rule to each set of events from each system whenthe event data format differs among systems.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

2.9. Acceleration Technique

The above-described system provides significant flexibility by enablinganalysis of massive quantities of minimally processed data “on the fly”at search time instead of storing pre-specified portions of the data ina database at ingestion time. This flexibility enables valuableinsights, data correlation, and the performance of subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.9.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 6 illustrates how a search query 602received from a client at a search head 210 can split into two phases,including: (1) subtasks 604 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 606 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 604, and then distributes searchquery 604 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 606 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.9.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.9.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “Distributed HighPerformance Analytics Store”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.9.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criterion, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “Compressed Journaling InEvent Tracking Files For Metadata Recovery And Replication”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “Real Time Searching AndReporting”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.10. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (STEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional STEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

2.11. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CorrelationFor User-Selected Time Ranges Of Values For Performance Metrics OfComponents In An Information-Technology Environment With Log Data FromThat Information-Technology Environment”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

2.12. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2, the networkedcomputer system 700 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system700, one or more forwarders 204 and client devices 702 are coupled to acloud-based data intake and query system 706 via one or more networks704. Network 704 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 702 and forwarders204 to access the system 706. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 706 forfurther processing.

In an embodiment, a cloud-based data intake and query system 706 maycomprise a plurality of system instances 708. In general, each systeminstance 708 may include one or more computing resources managed by aprovider of the cloud-based system 706 made available to a particularsubscriber. The computing resources comprising a system instance 708may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 702 to access a web portal or otherinterface that enables the subscriber to configure an instance 708.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 708) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.13. Searching Externally Archived Data

FIG. 8 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 804 over network connections820. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 8 illustrates thatmultiple client devices 804 a, 804 b, . . . , 804 n may communicate withthe data intake and query system 108. The client devices 804 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 8 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a system developer kit(SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 804 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 810. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 810, 812. FIG. 8 shows two ERP processes 810, 812 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 814 (e.g., Amazon S3, Amazon EMR, otherHadoop Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 816. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 810, 812indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 810, 812 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 810, 812 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 810, 812 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 810, 812 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices814, 816, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 804 may communicate with the data intake and query system108 through a network interface 820, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “ExternalResult Provided Process For RetriEving Data Stored Using A DifferentConfiguration Or Protocol”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.13.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. One examplequery language is Splunk Processing Language (SPL) developed by theassignee of the application, Splunk Inc. The search head typicallyunderstands how to use that language to obtain data from the indexers,which store data in a format used by the SPLUNK® Enterprise system.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. It Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. Anomaly Event Detection

As described above, embodiments are directed to an efficient techniquefor detecting anomalous events, such as events received by the dataintake and query system. For an event, multiple scores are calculated bycomparing the features in the event with multiple frequent patternsspanning one or more granularity levels. The multiple scores arecombined to calculate an aggregate score, and the aggregate score isused to determine whether the event is an anomaly (e.g., a potentialsecurity threat to the network, a fault or failure of one or morehardware and/or software components of the computing environment,inefficient use of resources). The frequent patterns are generated byexecuting one or more frequent itemset mining algorithms on historicevents that have been classified as normal (i.e., not classified asanomalies).

FIG. 9 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented. In FIG. 9, the datasources 202, data intake and query system 108, forwarder 204, indexer206, and search head 210 may be the same or similar to the like namedcomponents shown in FIG. 2. The event data store 208 of FIG. 9 may bethe same or similar to the data store 208 shown in FIG. 2. Further,although fewer instances of the components are shown, any number ofinstances may exist. For example, the data sources may be in thehundreds or thousands. Similarly, the number of forwarders, indexers,and data stores may similarly scale.

As shown in FIG. 9, the data intake and query system further includes ananalyzer 904. The data intake and query system 108 may correspond to aserver group having multiple servers. Each server may, for example,correspond to hardware. Thus, the forwarder 204, indexer 206, data store208, search head 210, and analyzer 904 may each be a server.

Continuing with FIG. 9, the analyzer 904 is communicatively connected tothe event data store 208. The analyzer 904 may be hardware, software, orfirmware, or any combination thereof that includes functionality toprocess event data to generate conclusions (e.g., anomalies, threatindicators, threats, or any combination thereof). The analyzer 904 mayoperate in real-time. “Real-time” computing, or “reactive computing”,describes computer systems subject to a processing responsivenessrestriction (e.g., in a service level objective (SLO) in a service levelagreement (SLA)). In real-time processing, conclusions are reachedsubstantially immediately following the receipt of input data such thatthe conclusions can be used to respond to the observed environment. Theanalyzer 904 continuously receives new events from the indexer 206 andreacts to each new incoming event by processing the event through ascoring model 908 and an anomaly detector 910. The analyzer 904 mayfurther analyze historical data. In such a scenario, the analyzer 904may use event data obtained from queries submitted to the search head210.

The analyzer 904 includes a classifier 906, scoring model 908, and ananomaly detector 910. The classifier 906 includes functionality toclassify events into activity types. In one or more embodiments, theclassifier 906 is configured to binary encode each event and thencluster the events into activity types by executing one or moreclustering algorithms on the binary encodings. The binary encoding foran event may be based on the types of features found in the event andthe types of features not present (i.e., missing) in the event.Additional details regarding the classifier 906 and the classifier'soperations are described below with reference to FIGS. 10, 11, and 18.

A scoring model 908 is hardware, software, firmware, or any combinationthereof that includes functionality to generate an aggregate score foran event. For example, the aggregate score may be generated based onmultiple scores for the event, and each of the multiple scores may bebased on one or more frequent patterns. Different scores of the multiplescores for an event may correspond to different sets of frequentpatterns associated with different levels of granularity. The scoringmodel 908 may also include functionality to generate the frequentpatterns by mining historic events for frequent features. Additionaldetails regarding the scoring model 908 and the scoring model'soperations are described below with reference to FIGS. 12, 13, 16, and19.

An anomaly detector 910 is hardware, software, firmware, or anycombination thereof that includes functionality to detect anomalies. Inthis description, an “anomaly” may be described as an event includingone or more features that are uncommon or unexpected for the activitytype associated with the event. In security, an anomaly might or mightnot be indicative of a threat. An anomaly represents an action ofpossible concern, which may be actionable or warrant furtherinvestigation. An anomaly is an observable or detectable fact, or datarepresenting such fact.

Continuing with the anomaly detector 910, the anomaly detector 910includes functionality to process events in real time and/or based onhistorical events. In one or more embodiments, the anomaly detector 910compares the aggregate scores of events with an anomaly score thresholdand issues alerts identifying events as anomalies. Further, the anomalydetector 910 may reclassify events from anomaly to normal (i.e., not ananomaly) when events with identical features appear frequently. Further,the anomaly detector 910 may increase the aggregate score for an event(indicating the event is of higher concern) when one or more conditionsare satisfied (e.g., this event contains features that occurred suddenlyin a high volume among certain users). Further, the anomaly detector 910may reduce the aggregate score for an event (indicating less concern,but still classified as anomaly) when one or more conditions satisfied(e.g., this event contains features that occurred with low frequencyamong certain users in the past a few days). Further still, the anomalydetector 910 may issue custom reports reporting events that occur with aspecific count within a time window. Additional details regarding theanomaly detector 910 and the anomaly detector's operations are describedbelow with reference to FIGS. 14 and 17.

FIG. 10 shows a diagram of a classifier 906, event data store 208, and asegregated event data store 1010. The classifier 906 and event datastore 208 may correspond to like named components of FIG. 9. The variouscomponents of FIG. 10 may be part of the data intake and query system asdescribed above or a distinct system. As shown in FIG. 10, the eventdata store 208 includes functionality to store events 1008. As discussedabove, an event includes a portion of machine-generated data and isassociated with a specific point in time (timestamp). Each event in theevent data store 208 is a recordation of activity of one or morecomputing devices. The events 1008 may include network traffic eventsincluding packets through one or more firewalls, packets routed by oneor more routers, etc. For example, each event may correspond to a singlepacket processed by the firewall on the network.

In one or more embodiments, an event has one or more features. A featuremay be a single value of a single data item in the event, a collectionof values of a data item in an event, or derived or otherwise determinedfrom a collection of values. Each feature may be of a particular featuretype. The type of feature defines the information that the featurerepresents.

Although not shown, additional data sources may be used by theclassifier 906. For example, the additional data may include events fromother logs (e.g., endpoint logs, operating system logs, and varioustypes of application logs), databases, among other examples. Theendpoint logs may provide information about processes running in amachine, and the operating system of a device. The operating system logsmay provide information about the activity in the device. Applicationlogs, such as database logs may be used to provide information about theapplications that are executing on each device.

The classifier 906 includes a binary encoder 1002 with a feature typelist 1006. The feature type list 1006 stores many or all of the types offeatures that may be present in the events 1008. Example feature typesmay include destination device, source device, port number, protocolused, application name, application type, etc. The binary encoder 1002is configured to generate a binary encoding for each of the events 1008.A binary encoding many have multiple entries and each entry cancorrespond to one of the feature types in the feature type list 1006. Ifan entry in a binary encoding is populated with a “1”, then thecorresponding event has a feature belonging to the feature typeassociated with the entry. If an entry in a binary encoding is populatedwith a “0”, then the corresponding event does not have a feature (i.e.,is missing a feature) belonging to the feature type associated with theentry. Alternatively, “0” may correspond to the existence of the featureand “1” may correspond to lack of the feature without departing from thescope of the disclosure. Example binary encodings are shown in FIG. 11.

In one or more embodiments, the clustering engine 1004 is configured togroup or cluster the events 1008 into activity types. In one or moreembodiments, an activity type is a task performed by a computing deviceor computer component. In one or more embodiments, an activity type is atask performed by the network or involving the network. For example,activity types may include logins, authentications, resource accessing,etc. Events grouped or clustered to the same activity type have similarfeature types. In contrast, different events belonging to differentactivity types have different feature types. In one or more embodiments,the clustering engine 1004 executes a clustering algorithm not on theevents themselves, but rather on the binary encodings to group orcluster the binary encodings into activity types. Once it is determinedwhich binary encodings belong to which activity types, the clustering ofthe corresponding events is known. Example clustering algorithms includek-means clustering, mean-shift clustering, density-based spatialclustering of applications with noise (DBSCAN), and power iterationclustering on the Jaccard distance.

As further shown in FIG. 10, the segregated event data store 1010 issoftware and/or hardware storage structure that includes functionalityto store the events 1008 grouped or clustered by activity types (i.e.,activity type 1 1020, activity type N 1030) following operation of theclustering engine 1004 or another process that clusters or groups theevents by activity types (i.e., activity type 1 1020, activity type N1030). In one or more embodiments, the segregated event data store 1010and the event data store 208 are part of the same physical storagedevice. In one or more embodiments, once the activity type for an eventhas been determined, the event (or a copy of the event) is moved fromthe event data store 208 to the segregated event data store 1010.

FIG. 11 shows example binary encodings in accordance with one or moreembodiments. Specifically, FIG. 11 shows a feature type list 1006corresponding to the like named component of FIG. 10. The feature typelist 1006 includes multiple feature types (i.e., FT 1 1191, FT 21192, FT3 1193, FT 4 1194, FTN 1195). FIG. 11 also shows multiple events (i.e.,event 1 1111, event 2 1112, event N 1113) including multiple features.For example, event 1 1111 includes features F1 1101, F2 1102, and FN1105. As another example, event 2 1112 includes features F2 1102, F31103, and F4 1104. As yet another example, event N 1113 includesfeatures F1 1101 and FN 1105. Assume F1 1101, F2 1102, F3 1103, F4 1104,and FN 1105 are features belong to feature types FT 1 1191, FT 2 1192,FT 3 1193, FT 4 1194, and FT N 1195, respectively.

Binary encoding 1 1121 corresponds to event 1 1111. Each entry in binaryencoding 1 1121 correspond to a feature type in the feature type list1006. The entries corresponding to FT 1 1191, FT 2 1192, and FT N 1195are set to “1” because event 1 1111 includes features belonging to FT 11191, FT 2 1192, and FT N 1195. The remaining entries in binary encoding1 1121 are set to “0” because event 1 1111 is missing features belongingto FT 3 1193 and FT 4 1194.

Binary encoding 2 1122 corresponds to event 2 1112. Each entry in binaryencoding 2 1122 correspond to a feature type in the feature type list1006. The entries corresponding to FT 2 1192, FT 3 1193, and FT 4 1194are set to “1” because event 2 1112 includes features belonging to FT 21192, FT 3 1193, and FT 4 1194. The remaining entries in binary encoding2 1122 are set to “0” because event 2 1112 is missing features belongingto FT 1 1191 and FTN 1195.

Binary encoding N 1123 corresponds to event N 1113. Each entry in binaryencoding N 1123 correspond to a feature type in the feature type list1006. The entries corresponding to FT 1 1191 and FT N 1195 are set to“1” because event N 1113 includes features belonging to FT 1 1191 and FTN 1195. The remaining entries in binary encoding N 1123 are set to 0because event 3 1113 is missing features belonging to FT 2 1192, FT 31193, and FT 4 1194.

FIG. 12 shows a diagram of scoring model 908, segregated event datastore 1010, frequent pattern data store 1220, and historic events datastore 1230. In one or more embodiments, two or more of the segregatedevent data store 1010, the frequent pattern data store 1220, and thehistoric events data store 1230 are part of the same physical storagedevice. Scoring model 908 and segregated event data store 1010correspond to the like named components of FIG. 9 and FIG. 10. Thefrequent pattern data store 1220 of FIG. 12 includes functionality tostore multiple frequent patterns. Each of the frequent patterns includesone or more features and each of the frequent patterns may be obtainedby executing one or more frequent itemset mining algorithms on historicevents (discussed below). As shown in FIG. 12, the frequent patterns areorganized by granularity levels (i.e., granularity level 1 frequentpatterns 1221 granularity level K frequent patterns 1222). Eachgranularity level includes one or more frequent patterns. Some frequentpatterns may belong to multiple granularity levels. In one or moreembodiments, each frequent pattern includes one or more features ofdifferent feature types. Moreover, there may be feature overlap betweenpatterns. For example, one frequent pattern may consist of the feature{a}, while another frequent pattern may consist of the features {a, b},while yet another frequent pattern may consist of the features {a, b,c}. The length of the frequent pattern is the number of features in thepattern. For example, the length of the frequent pattern {a, b, c} isthree, while the length of frequent pattern {a} is one.

In one or more embodiments, each activity type is associated with a setof frequent patterns spread across multiple granularity levels. Forexample, activity type 1 1020 may be associated with granularity level 1frequent patterns 1221 through granularity level K frequent patterns1222. As another example, activity type N 1030 may be associated with adifferent set of frequent patterns (not shown) spread across multiplelevels and also stored in frequent pattern data store 1220. Additionaldetails regarding granularity levels are discussed below.

In one or more embodiments, the scoring model 908 includes scoringengine 1212. The scoring engine 1212 includes functionality to calculatemultiple scores for each event. Specifically, each score for an eventmay be calculated based on pattern matching between the features in theevent and the frequent patterns belonging to a granularity level. Forexample, assume there is an event belonging to activity type 1 1020. Onescore for the event may be calculated based on pattern matching betweenthe features in the event and the frequent patterns in granularity level1 frequent patterns 1221. Another score for the event may be calculatedbased on pattern matching between the features in the event and thefrequent patterns in granularity level K frequent patterns 1222.Additional details regarding calculating/determining scores arediscussed below and in reference to FIG. 15. Each score may be referredto as a level specific score as each score is associated with frequentpatterns in a single granularity level.

In one or more embodiments, the scoring engine 1212 also includes ablack list 1215 and optionally one or more weights 1213. The black list1215 stores features known to be threats. For example, features on theblack list 1215 may correspond to known security threats (e.g.,malicious users, malicious IP addresses, malicious devices, maliciousapplications, etc.). The weights 1213 may correspond to the features onthe black list 1215. If the event includes a feature on the black list1215, one or more scores for the event may further be based on theweight assigned to the feature on the black list 1215.

In one or more embodiments, the scoring engine 121 is further configuredto calculate an aggregate score for each event. The aggregate score isbased on the multiple scores (i.e., the level-specific scores) for theevent. For example, the aggregate score for an event may be the sum ofthe multiple scores for the event. As another example, the aggregatescore may be the product of the multiple scores for the event. As yetanother example, the aggregate score may be the average or median of themultiple scores (i.e., the level-specific scores). The aggregate scorefor an event may be normalized to a value that is between 0 and 1. Inone or more embodiments, the lower the aggregate score, the less likelythe event is an anomaly. In contrast, the higher the aggregate score,the more likely the event is an anomaly. An event that is not classifiedas an anomaly may be referred to as “normal” or a “normal event.” Anevent is “normal” when the event occurs with a certain degree offrequency (e.g., above a threshold number of times) and regularity overperiods of time, such that it can be said that the event is part of theevery day, legitimate activity of a computing environment. In thisrespect, and event that occurs with a high degree of frequency over ashort period of time, or occurs infrequently but regularly, may not benormal events, and may be anomalies.

The historic events data store 1230 includes functionality to storemultiple historic events that were deemed to be normal. In other words,the historic events data store 1230 may exclude historic events thatwere deemed to be anomalies. Within a single activity type, there may bemultiple granularity levels. The granularity level defines the degree ofmatching events for the purposes of anomaly detection. In broad terms,an anomaly is an outlier in comparison to other events in a grouping.The granularity level defines which other events are in the grouping.The multiple granularity levels form a hierarchy within the activitytype. For example, the broadest granularity level (e.g., the granularitylevel at the top of the hierarchy) may be a global level that includesall historic events within the single activity type. Other granularitylevels may be more narrow (e.g., the other granularity levels are lowerin the hierarchy). For example, some granularity levels may be userspecific and only include historic events associated with a single user.As another example, some granularity levels may be device or applicationspecific and, thus, only include historic events associated with asingle device or single application. If the historic events areassociated with commerce, some granularity levels may be merchantspecific and thus only include historic events associated with thesingle merchant. An historical event may belong to more than onegranularity level. In one or more embodiments, granularity level 1historic events 1231 and granularity level K historic events 1232 areall historic events of activity type 1 1020. The historic events datastore 1230 may also store historic events (not shown) belonging toactivity type N 1030.

In one or more embodiments, the scoring model 908 includes the frequentpattern generator 1214. The frequent pattern generator 1214 includesfunctionality to generate frequent patterns based on historic events.Specifically, the frequent pattern generator 1214 may execute one ormore frequent itemset mining algorithms to identify frequent patterns offeatures. Specifically, the frequent pattern generator 1214 generatesgranularity level 1 frequent patterns 1221 by executing one or morefrequent itemset mining algorithms on granularity level 1 historicevents 1231. Similarly, the frequent pattern generator 1214 generatesgranularity level K frequent patterns 1222 by executing one or morefrequent itemset mining algorithms on granularity level K historicevents 1232. Example frequent itemset mining algorithms include Apriori,Frequent Pattern (FP) Growth, Equivalence Class Transformation, TreeProjection, Co-Occurrence Frequent Itemset, etc. The outputs of afrequent itemset mining algorithm are frequent patterns of variouslengths.

FIG. 13 shows an example of granularity level 1 frequent patterns 1221.Frequent pattern 1 1301, frequent pattern 2 1302, frequent pattern 31303, and frequent pattern M 1304 were identified by executing afrequent itemset mining algorithm on granularity level 1 historic events1231. As shown in FIG. 13, some features (e.g., F1 1101) belong tomultiple frequent patterns. Moreover, different frequent patterns may beof different lengths. For example, frequent pattern M 1304 has a lengthof 3, while frequent pattern 2 1302 and frequent pattern 3 1303 eachhave a length of 2, and frequent pattern 1 1301 has a length of 1.

Referring back to FIG. 12, those skilled in the art, having the benefitof this detailed description, will appreciate that minimum support(minSupport) is a parameter used by frequent itemset mining algorithmsto identify frequent patterns of features. The minimum support parametercontrols the frequency with which a pattern must appear before thepattern can be considered a frequent pattern. A low minSupport valueresults in many frequent patterns, while a large minSupport valueresults in few frequent patterns. In one or more embodiments, thefrequent pattern generator includes functionality to calculate theminSupport value based on the desired number of frequent patterns andthe number of historic events. Additional details regarding thecalculation of the minSupport value are discussed below in reference toFIG. 19.

FIG. 14 shows a diagram of anomaly detector 910, low score events datastore 1404, and high score events data store 1406. Anomaly detector 910corresponds to the like named component of FIG. 9. In one or moreembodiments, the anomaly detector 910 of FIG. 14 includes functionalityto determine whether an event is an anomaly or normal. Specifically, theanomaly detector 910 may compare the aggregate score of the event withan anomaly score threshold to determine whether the event is an anomaly.

In one or more embodiments, the low score events data store 1404includes functionality to store events that have been deemed normal(i.e., not anomalies). The anomaly detector 910 may periodicallytransfer the low score events data store 1404 into the historic eventsdata store 1230, discussed above in reference to FIG. 12, for use ingenerating frequent patterns. In contrast, the high score events datastore 1406 includes functionality to store events that have been deemedanomalies. In one or more embodiments, one or more frequent itemsetmining algorithms may be executed on the events in the high score eventsdata store 1406 (i.e., executed on the anomalous events). The outputfrom executing these algorithms is one or more anomaly frequent patterns(e.g., new frequent patterns) that may be included in custom reports ofthe anomaly detector 910.

In one or more embodiments, the anomaly detector 910 includes an anomalytable 1402. The anomaly table 1402 may track or record events that havebeen deemed anomalies. The anomaly table 1402 may also store a count asto how many times anomalous events with identical features have occurredwithin a time window (e.g., within the last 24 hours, within the last 48hours, within the last week, within the last month, within the last sixmonths, since the system has been operational, etc.). For example,assume the time window is one week and anomalous events E1={a, b, c},E2={a, b, c}, and E3={a, b, c} have all been processed within the lastweek. In this example, the count for anomalous events with identicalfeatures {a, b, c} would be 3. In one or more embodiments, when thecount for anomalous events with identical features exceeds a maximumcount, these identical features may be deemed to have appeared toofrequently to be considered anomalies. The anomaly detector 910 mayinclude functionality to reduce the aggregate scores for these eventsbelow the anomaly score threshold and then store these events in the lowscore events data store 1404.

In one or more embodiments, the anomaly table 1402 may also store countsas to the number of users, devices, and/or applications that have seenanomalous events with identical features within a time window. When oneof these counts exceeds a count threshold, an event with these identicalfeatures may be deemed to be less suspicious. The anomaly detector 910may reduce the aggregate score of the event by a default amount (e.g.,10%, 17%, etc.). Following the reduction, if the aggregate score isstill in excess of the anomaly score threshold, the event is stilldeemed an anomaly and stored in the high score events data score 1406.Otherwise, the event may now be deemed normal and stored in the lowscore events data store 1404.

In one or more embodiments, the anomaly detector 910 includesfunctionality to issue alerts identifying the events in the high scoreevents data store 1406 as anomalies. The anomaly detector 910 may alsoinclude functionality to issue custom reports including statistics aboutany of the data tracked in the anomaly table 1402.

FIGS. 15-19 show flowcharts in accordance with disclosed embodiments.While the various steps in these flowcharts are presented and describedsequentially, one of ordinary skill will appreciate that some or all ofthe steps may be executed in different orders, may be combined oromitted, and some or all of the steps may be executed in parallel.Furthermore, the steps may be performed actively or passively. Forexample, some steps may be performed using polling or be interruptdriven in accordance with one or more embodiments of the invention. Byway of an example, determination steps may not require a processor toprocess an instruction unless an interrupt is received to signify thatcondition exists in accordance with one or more embodiments of theinvention. As another example, determination steps may be performed byperforming a test, such as checking a data value to test whether thevalue is consistent with the tested condition in accordance with one ormore embodiments of the invention.

FIG. 15 is a flow diagram that illustrates operation of the analyzer 904in accordance with disclosed embodiments. The process depicted in FIG.15 may, in part, be used to identify events that are anomalies.

In Block 1502, an event log with multiple events is received. Each eventincludes a portion of machine-generated data and is associated with atimestamp. Moreover, each event is a recordation of an action performedby or at one or more devices. The events may include, for example,network traffic events including packets through one or more firewalls,packets routed by one or more routers, etc. Further, each event includesone or more features.

The multiple events of the event log may belong to the same or differentactivity types. For example, activity types may include logins,authentications, resource accessing, etc. Events grouped or clustered tothe same activity type have similar feature types. In contrast, eventsfrom belonging to different activity types may have different featuretypes. In one or more embodiments, binary encoding is used to cluster orgroup the events by activity type.

In Block 1504, multiple scores are determined (e.g., calculated) for anevent by comparing the event with multiple frequent patterns fromdifferent granularity levels. As discussed above, for each activitytype, there may be multiple frequent patterns spread across multiplegranularity levels. Each of the multiple scores for an event may bedetermined (e.g., calculated) based on pattern matching between thefeatures in the event and the frequent patterns belonging to one of thegranularity levels. For example, assume the event belongs to activitytype A, and there are P granularity levels (described above withreference to FIG. 12) associated with activity type A. Accordingly, Pscores will be determined (e.g., calculated) for the event (i.e., onescore for each of the P granularity levels). The number/type ofgranularity levels and thus the frequent patterns may be different fordifferent activity types. Each of the scores determined (e.g.,calculated) in Block 1504 may be referred to as a level-specific score.Additional details regarding score determination (e.g., calculation) isdiscussed below in reference to FIG. 16.

In Block 1506, an aggregate score is determined (e.g., calculated) forthe event. The aggregate score for the event is determined (e.g.,calculated) based on the multiple level-specific scores for the event.In one or more embodiments, the larger the aggregate score, the morelikely the event is an anomaly. The aggregate score for the event may bedetermined (e.g., calculated), for example, by summing or averaging themultiple level-specific scores for the event. Additionally oralternatively, the aggregate score for the event may be determined(e.g., calculated) by multiplying the multiple level-specific scores forthe event. Additionally or alternatively, the aggregate score for theevent may be the maximum of the level-specific scores. Additionally oralternatively, the aggregate score for the event may be the maximum of asubset of the level-specific scores averaged with another level-specificscore. The aggregate score may be optionally normalized between a valueof 0 and 1.

In Block 1508, the aggregate score for the event is compared with ananomaly score threshold. When it is determined that the aggregate scoresatisfies (e.g., equals or exceeds) the anomaly score threshold, theprocess proceeds to Block 1510. When it is determined that the aggregatescore does not satisfy (e.g., is less than) the anomaly score threshold,the process proceeds to Block 1512. Different activity types may utilizedifferent anomaly score thresholds. One or more of the anomaly scorethresholds may be user specified.

In Block 1510, the event has been deemed an anomaly and an alert isissued identifying the event as an anomaly. The alert may be displayed,stored, and/or transmitted (e.g., emailed) to a user. The alert may beincluded on a report. The alert may be reported to another applicationthat takes action based on the alert. The alert may be specific to thisevent. Additionally or alternatively, there may be a single alertcovering multiple anomalous events.

In Block 1512, the event has been deemed normal (i.e., not an anomaly).In one or more embodiments, the event may be used to update a scoringmodel. Specifically, the event may be added to the historic events datastore 1230 and used to generate or update frequent patterns in thefuture.

Blocks 1504, 1506, and 1508 may be repeated for each event in the eventlog. As discussed above, the event log may include events from one ormore incoming data streams (e.g., data streams from different networkdevices). Moreover, as also disclosed above, these events may belong todifferent activity types. The number/type of granularity levels and thusthe frequent patterns in Block 1504 may differ depending on the activitytype to which the event belongs. Further, the algorithm for determining(e.g., calculating) the aggregate score in Block 1506 may differdepending on the activity type to which the event belongs. Furtherstill, the anomaly score threshold in Block 1508 may also differdepending on the activity type to which the event belongs.

FIG. 16 is a flow diagram that illustrates operation of the scoringmodel 908 in accordance with disclosed embodiments. The process depictedin FIG. 16 may be used, in part, to determine (e.g., calculate) alevel-specific score for an event. One or more of the Blocks in FIG. 16may correspond to Block 1504 in FIG. 15. Prior to starting the processdepicted in FIG. 16, an event E has been selected from an event log andthe activity type to which event E belongs is also known.

In Block 1602, one or more frequent patterns and one or more unmatchedfeatures within the event are identified. In one or more embodiments,identifying the frequent patterns includes comparing the event withfrequent patterns belonging to a single granularity level. Any remainingfeatures in the event that do not match one of the frequent patterns areunmatched features.

For example, assume event E={a, b, c, d, e}, where a, b, c, d, and e arefeatures. For example, event E={Network, KerberosProcess,isDeviceWithADDomain, msspinfoapp, 168.136.162.24}. Further, assumefrequent patterns FP1={x, y, z}, FP2={a}, FP3={a, b}, and FP4={a, b, c}are all frequent patterns in a single granularity level for the activitytype to which event E belongs. Accordingly, in this example, thefrequent patterns FP2, FP3, and FP4 are identified in the event.However, the features {d} and {e} in the event E do not match any of thefrequent patterns and thus are unmatched features.

In Block 1604, the length of each identified frequent pattern isdetermined. The length of an identified frequent pattern is the numberof features in the frequent pattern. Returning to the example from Block1602, the length of FP2 is 1, the length of FP3 is 2, and the length ofFP4 is 3. Len(FPi) means the length of identified frequent pattern i(e.g., Len(FP3)=2).

In Block 1608, a count for each of the identified frequent patterns isidentified. The count is the number of times the identified frequentpattern appears in events belonging to the same activity type. Returningto the example from Block 1602 and 1604, the count for FP2 is the numberof times FP2 appears in the events belonging to the same activity typeas event E. Similarly, the count for FP3 is the number of times FP3appears in the events belonging to the same activity type as event E.Similarly, the count for FP4 is the number of times FP4 appears in theevents belonging to the same activity type as event E. Count(FPi) meansthe count of identified frequent pattern i.

In Block 1610, a contribution is determined (e.g., calculated) for eachof the identified frequent patterns. In one or more embodiments, thecontribution for identified FPi=k/[γLen(FPi)*βSupport(FPi)], whereSupport(FPi)=Count(FPi)/(total number of events belonging to the sameactivity type as event E), and where k, γ, and β are constants. In oneor more embodiments k=γ=β=1.

In Block 1612, a penalty is determined (e.g., calculated) for eachidentified unmatched feature. In one or more embodiments, the penaltyfor an unmatched feature is the total number of events (i.e.,cardinality of events) in the activity type to which the event Ebelongs. Additionally or alternatively, the penalty for an unmatchedfeature may be a constant (e.g., 0.75, 2.7, 50, etc.). In other words,the penalty may be the same for each unmatched feature and unrelated tothe total number of events.

In Block 1614, weights may be applied to one or more of the penalties.As discussed above, the scoring engine 1212 may maintain a black list1215 identifying suspicious features. As also discussed above, thescoring engine 1212 may maintain weights 1213 for features on the backlist 1215. In one or more embodiments, if an unmatched feature is on theblack list 1215, the penalty for the unmatched feature, as determined(e.g., calculated) in Block 1612, is scaled (e.g., increased) by thecorresponding weight for the unmatched feature stored in weights 1213.In one or more embodiments, Block 1614 is only executed if the scoringengine 1212 maintains a black list 1215 of features and weights 1213.

In Block 1616, an average of the contributions and penalties isdetermined (e.g., calculated). If weights were applied in Block 1614,the determined (e.g., calculated) average is effectively a weightedaverage. In one or more embodiments, the determined (e.g., calculated)average of Block 1614 is a level-specific score for event E.

In one or more embodiments, Blocks 1602-1616 are repeated multiple timesfor a single event E. During different iterations, the single event E iscompared with frequent patterns from different granularity levels (e.g.,granularity level 1 frequent patterns 1221, granularity level K frequentpatterns 1222, etc.). Accordingly, the average determined (e.g.,calculated) during each iteration of Block 1616 may be a score (forevent E) specific to one granularity level. If there are K granularitylevels for the activity type to which event E belongs, Blocks 1602-1616may be executed K times and K level-specific scores for event E may bedetermined (e.g, calculated).

FIG. 17 is a flow diagram that illustrates operation of the anomalydetector 910 in accordance with disclosed embodiments. The processdepicted in FIG. 17 may be used, in part, to process potentiallyanomalous events. Prior to staring the process depicted in FIG. 17 amaximum count (e.g., 10, 15, 19, etc.) for an event and a time window ofany duration (e.g., 2 hours, 3 days, 1 week, 1 month, etc.) has beenspecified (e.g., by a user, by an application, etc.). Both the maximumcount and the time window may be stored within the anomaly table 1402.

In Block 1506, an aggregate score is determined (e.g., calculated) foran event. The aggregate score for the event is determined (e.g.,calculated) based on the multiple level-specific scores for the event.In one or more embodiments, the larger the aggregate score, the morelikely the event is an anomaly. Additional details regarding Block 1506are discussed above in reference to FIG. 15.

In Block 1508, the aggregate score for the event is compared with ananomaly score threshold. When it is determined that the aggregate scoresatisfies (e.g., equals or exceeds) the anomaly score threshold, theprocess proceeds to Block 1702. When it is determined that the aggregatescore does not satisfy (e.g., is less than) the anomaly score threshold,the process proceeds to Block 1512. Additional details regarding Block1508 and Block 1512 are discussed above in reference to FIG. 15.

In Block 1702, a count is determined. In one or more embodiments, thecount is the number of times anomalous events with identical features tothe anomalous event from Block 1508 have been processed during aspecific time window. Determining the count may include searching one ormore data stores (e.g., high score events data store 1406) looking foranomalous events with identical feature to the anomalous event fromBlock 1508. Additionally or alternatively, determining the count mayinclude accessing a table (e.g., anomaly table 1402) that tracks/recordsthe processed anomalous events.

In Block 1704, the count is compared with a maximum count. When it isdetermined that the count satisfies (e.g., equals or exceeds) themaximum count, the process proceeds to Block 1702. When it is determinedthat the count does not satisfy (e.g., is less than) the maximum count,the process proceeds to Block 1510. Additional details regarding Block1510 are discussed above in reference to FIG. 15.

Those skilled in the art, having the benefit of this detaileddescription, will appreciate that over time, when anomalous events withidentical features have been recorded/observed many times (e.g.,equaling or exceeding the maximum count), it might no longer be properto classify these events as anomalies. Accordingly, in Block 1706, theaggregate score of the event is reduced (e.g., by 10%, 21%, or any otherspecified amount). The absolute or percentage amount for the reductionmay be specified by a user prior to execution of the proceed depicted inFIG. 17.

In Block 1708, the reduced aggregate score for the event is comparedwith the anomaly score threshold. When it is determined that the reducedaggregate score still satisfies (e.g., equals or exceeds) the anomalyscore threshold, the process proceeds to Block 1510. When it isdetermined that the reduced aggregate score no longer satisfies theanomaly score threshold, the process proceeds to Block 1512. In otherwords, the event is no longer deemed to be an anomaly. Instead, theevent is deemed to be normal. In Block 1512, the now-normal event isused to update the scoring model. Additional details regarding Block1512 are discussed above in reference to FIG. 15.

The process depicted in FIG. 17 focuses heavily on tracking a singlecount (i.e., the number of times anomalous events with identicalfeatures have been processed) during a time window. In one or moreembodiments, additional counts are also tracked. For example, the numberof users that have observed anomalous events with identical featuresduring a time window may be tracked, the number of devices that observedanomalous events with identical features during a time window may betracked, the number of applications that observed anomalous events withidentical features during a time window may be tracked, etc. If one ormore of these counts exceed their respective thresholds during the timewindow, then the aggregate score for the event from Block 1508 may bereduced (e.g., by 10%, 21%, or any other specified amount). Thoseskilled in the art, having the benefit of this detailed description,will appreciate that if anomalous events with identical features to theevent from Block 1508 have been observed historically across many users,devices, applications, etc., the event from Block 1508 is lesssuspicious and less likely to be an anomaly. Reducing the aggregatescore for the event might or might not bring the aggregate score belowthe aggregate score threshold, and thus might reclassify the event annormal.

Although the process depicted in FIG. 17 focuses heavily on reducing theaggregate score for an event, in additional or alternative embodiments,the aggregate score for an event may be increased (e.g., 105%, 138%,200%, etc.). For example, if the event contains features that haveappeared in many events among many different devices within a short timewindow (e.g., 24 hours), this may indicate the event is suspicious. Ifthe aggregate score for the event is not already in excess of theaggregate score threshold, the increase may place the aggregate score inexcess of the aggregate score threshold, and thus the event may bedeemed an anomaly.

FIG. 18 is a flow diagram that illustrates operation of the classifier906 in accordance with disclosed embodiments. The process depicted inFIG. 18 may be used, in part, to cluster events by activity type usingbinary encoding. One or more of the blocks in FIG. 18 may correspond toBlock 1502, discussed above in reference to FIG. 15.

In Block 1802, multiple events are obtained. The events may be obtainedvia an event log. Each event includes a portion of machine-generateddata and is associated with a specific point in time. Moreover, eachevent includes one or more features. Different events may belong todifferent activity types. However, the events might not be grouped byactivity type within the event log.

In Block 1804, a binary encoding is generated for each event. A binaryencoding many have multiple entries and each entry corresponds to adifferent feature type. If an entry in a binary encoding is populatedwith a “1”, this means the corresponding event has a feature belongingto the feature type associated with the entry. If an entry in a binaryencoding is populated with a “0”, this means the corresponding eventdoes not have a feature (i.e., is missing a feature) belonging to thefeature type associated with the entry. Example binary encodings areshown in FIG. 11. Example feature types may include destination device,source device, port number, protocol used, application name, applicationtype, etc.

In Block 1806, the events are grouped/clustered by activity types. Forexample, activity types may include logins, authentications, resourceaccessing, etc. Those skilled in the art, having the benefit of thisdetailed description, will appreciate that events grouped or clusteredto the same activity type have similar feature types. In contrast,different events belonging to different activity types have differentfeature types. In one or more embodiments, the events are group byexecuting a clustering algorithm. However, the clustering algorithm isnot executed on the events themselves, but rather on the binaryencodings. Once it is determined which binary encodings belong to whichactivity types, the clustering of the corresponding events is known.Example clustering algorithms include k-means clustering, mean-shiftclustering, density-based spatial clustering of applications with noise(DBSCAN), and power iteration clustering on the Jaccard distance.

Those skilled in the art, having the benefit of this detaileddescription, will appreciate that the process depicted in FIG. 18enables one or more disclosed components or processes to handle eventsof different activity types. In one or more embodiments, the processdepicted in FIG. 18 is executed on the incoming events (i.e., the eventsin the event log) because none of the events include a feature thatdirectly identifies the activity type to which the event belongs. Inother words, none of the events include a feature or feature type thatis unique to one activity type. In contrast, if an event includes afeature that is unique to one activity type, the event may be excludedfrom the process depicted in FIG. 18 because the activity type to whichthe event belongs is known. For example, if an event includes anauthentication request as a feature and the authentication request isunique to the authentication activity type, the event belongs to theauthentication activity type and there is no need for the processdepicted in FIG. 18 to be executed on the event.

FIG. 19 is a flow diagram that illustrates operation of the frequentpattern generator 1214 in accordance with disclosed embodiments. Theprocess depicted in FIG. 19 may be used, in part, to determined (e.g.,calculate) a minimum support value for use in a frequent itemset miningalgorithm and generate frequent patterns. One or more of the blocks inFIG. 19 may be executed before Block 1504, discussed above in referenceto FIG. 15.

In Block 1902, historic events of one granularity level within oneactivity type are obtained. These historic events may be obtained fromhistoric events data store 1230, discussed above in reference to FIG.12. The number of historic events may be in the hundreds, thousands,tens of thousands, hundreds of thousands, millions, etc. Each of thehistorical events includes one or more features.

In Block 1904, multiple data points are generated by executing frequentitemset mining on M different subsets of the historic events using Ndifferent minimum support values. The different subsets may havedifferent sizes (i.e., different cardinalities). As discussed above,minimum support is a parameter used by frequent itemset miningalgorithms to identify frequent patterns of features. This parametercontrols how often a pattern must appear before it can be considered afrequent pattern. A low minimum support value results in many frequentpatterns, while a large minimum support value results in few frequentpatterns.

Still referring to Block 1904, as an example, six subsets of historicevents may be selected (M=6) and these six subsets are of differentsizes: 50, 100, 250, 1000, 5000, and 100000 historic events. Moreover,five minimum support values may be selected (N=5): 0.001, 0.01, 0.1,0.2, and 0.5. The six subsets and five minimum support values allow for30 different scenarios/combinations (i.e., M×N=6×5=30).

For each scenario, the selected subset of historic events and theselected minimum support value are inputs to a frequent itemset miningalgorithm (e.g., Apriori algorithm, FP Growth algorithm, etc.). Theoutputs of the frequent itemset mining algorithm are one or morefrequent patterns. Accordingly, for each of the M×N scenarios, thereexists a triplet data point: (number of historical events in subset,number of frequent patterns, minimum support value). Each triplet datapoint may be rewritten as: (number of historical events in subset,ratio, minimum support value), where ratio=number of frequentpatterns/number of historical events in subset. Following the executionof Block 1904, there are M×N (e.g., 30) triplet data points eachcorresponding to one of the scenarios.

In Block 1906, a formula is determined for determining (e.g.,calculating) the minimum support value based on a ratio and a number ofevents. In other words, the ratio and the number of events are inputs tothe formula, while the minimum support value is the output of theformula. In one or more embodiments, it is assumed that the relationshipbetween the ratio and both the number of events (#events) and theminimum support value (minSupport) is an exponential decaying functionin the form:ratio=exp(−a1*#events+b1)*exp(−a2*minSupport+b2).

This may be rewritten as:log(ratio)=−a1*#events−a2*minSupport+(b1+b2)

Estimators of a1, a2, b1+b2 may be determined using the M×N triplet datapoints and mean squared error (MSE) loss. The formula may then bearranged as:minSupport=const1*#events+const2*log(ratio)+const3

In other words, the formula determines (e.g., calculates) the minimumsupport value based on two inputs: number of events (#events) and aratio of the desired number of frequent patterns to the number ofevents. Additionally, an upper boundary (i.e., upperBound) and a lowerboundary (i.e., lowerBound) may be set for minSupport. This results in:minSupport=Min(Max(calculated minSupport,lowerBound),upperBound)

In Block 1908, the desired number of frequent patterns for thegranularity level and activity type mentioned in Block 1902 is obtained.The desired number of frequent patterns may be a setting specified by auser or provided by an application at any time prior to execution ofBlock 1910.

In Block 1910, a minimum support value is determined (e.g., calculated)using the formula of Block 1906, the number of historic events obtainedin Block 1902, and the desired number of frequent patterns obtained inBlock 1908. If not directly provided, the ratio of the desired number offrequent patterns and the number of historic events is determined (e.g.,calculated) for use in the formula.

In Block 1912, the frequent patterns for the granularity level andactivity type mention in Block 1902 are generated. The frequent patternsare generated by submitting the historic events of Block 1902 and theminimum support value determined (e.g., calculated) in Block 1910 to afrequent itemset mining algorithm (e.g., Apriori algorithm, FP Growthalgorithm, etc.). The frequent patterns generated in Block 1912 maycorrespond, for example, to granularity level 1 frequent patterns orgranularity level K frequent patterns 1222, discussed above in referenceto FIG. 12. In other words, the frequent patterns generated in Block1912 may be stored in the frequent pattern data store 1220 and used togenerate a level-specific score for an event in the segregated eventdata store 1010.

The process depicted in FIG. 19 may be repeated for each granularitylevel of each activity type. Following the multiple executions of theprocess depicted in FIG. 19, the frequent pattern data store 1220 mayinclude frequent patterns for every granularity level of an activitytype. Moreover, these frequent patterns may be used to generatelevel-specific scores for each of the events in the segregated eventdata store 1010.

Those skilled in the art, having the benefit of this detaileddescription, will appreciate that the process depicted in FIG. 19enables one or more disclosed components or processes to determine(e.g., calculate) the minimum support value that will generate thenumber of frequent patterns requested by a user or external application.

The following example is not meant to be limiting in any way. Initially,network events are received in real time. Thousands and thousands ofnetwork events are being received every second. Each of the events hasmultiple features of one or more feature types (e.g., IP addresses,users names, MAC addresses, passwords, resource requests, URLs, etc.).These network events may be received from multiple components on thenetwork. These events are recorded in an event log.

Different events may belong to different activities types (e.g., logins,authentications, resource access requests). However, when the events arerecorded in the event log, it is unknown as to which events belong towhich activity types. In order to cluster the events by activity type, abinary encoding is generated for each event and then a power iterationclustering algorithm is executed on the binary encodings. As each binaryencoding corresponds to one of the events, once the binary encodings areclustered by activity type, it is known which events belong to whichactivity type.

Within a single activity type (e.g., logins), there may be one or moreevents that are anomalies. In other words, the anomalous events aredifferent from the rest of the events also belonging to the sameactivity type. In some cases, an anomalous event is benign. However, inother cases, an anomalous event may be a security treat (e.g., a hacker,denial-of-service attack, etc.). Accordingly, it is important to quicklyand efficiently identify anomalous events among all the events belongingto an activity type.

The level-specific scores and aggregate scores assigned to events, asdiscussed above, are used to identify anomalous events. These scores arebased on frequent patterns that are activity type specific. For example,a frequent pattern for the login activity type may include an IPaddress, an device identifier, and an application identifier. Moreover,the frequent patterns for an activity type may span multiple granularitylevels. An event with a large aggregate score is likely to be ananomalous event and a security threat. Accordingly, the system may takeaction to mitigate the security threat (e.g., discard/ignore all futurerequests from the device/application/user associated with the anomalousevent, terminate the session of a device/application/user associatedwith the anomalous event, transmit alerts to IT personnel or otherapplications identifying a device/application/user associated with theanomalous event, etc.)

When some threshold count (e.g., 1002) of events with identical featuresare deemed to be anomalous within a time window (e.g., 6 hours), this isan indication that events with these identical features are actuallynormal (i.e., not anomalous). Accordingly, these previously-consideredanomalous events may be used to update the frequent patterns used todetermine (e.g., calculate) the level-specific and aggregates scores.This is effectively a feedback loop that improves the frequent patternsand reduces the likelihood of false positives (i.e., events deemedanomalous when the events are actually normal).

4.0 Hardware

The various components of the figures may be a computing system orimplemented on a computing system. For example, the operations of thedata stores, indexers, search heads, host device(s), client devices,data intake and query systems, data sources, external resources, and/orany other component shown and/or described above may be performed by acomputing system. A computing system may include any combination ofmobile, desktop, server, router, switch, embedded device, or other typesof hardware. For example, the computing system may include one or morecomputer processors, non-persistent storage (e.g., volatile memory, suchas random access memory (RAM), cache memory), persistent storage (e.g.,a hard disk, an optical drive such as a compact disk (CD) drive ordigital versatile disk (DVD) drive, a flash memory, etc.), acommunication interface (e.g., Bluetooth interface, infrared interface,network interface, optical interface, etc.), and numerous other elementsand functionalities. The computer processor(s) may be an integratedcircuit for processing instructions. For example, the computerprocessor(s) may be one or more cores or micro-cores of a processor. Thecomputing system may also include one or more input devices, such as atouchscreen, keyboard, mouse, microphone, touchpad, electronic pen, orany other type of input device.

The computing system may be connected to or be a part of a network. Forexample, the network may include multiple nodes. Each node maycorrespond to a computing system, such as the computing system, or agroup of nodes combined may correspond to the computing system. By wayof an example, embodiments of the disclosure may be implemented on anode of a distributed system that is connected to other nodes. By way ofanother example, embodiments of the disclosure may be implemented on adistributed computing system having multiple nodes, where each portionof the disclosure may be located on a different node within thedistributed computing system. Further, one or more elements of theaforementioned computing system may be located at a remote location andconnected to the other elements over a network.

The node may correspond to a blade in a server chassis that is connectedto other nodes via a backplane. By way of another example, the node maycorrespond to a server in a data center. By way of another example, thenode may correspond to a computer processor or micro-core of a computerprocessor with shared memory and/or resources.

The nodes in the network may be configured to provide services for aclient device. For example, the nodes may be part of a cloud computingsystem. The nodes may include functionality to receive requests from theclient device and transmit responses to the client device. The clientdevice may be a computing system. Further, the client device may includeand/or perform all or a portion of one or more embodiments of thedisclosure.

Software instructions in the form of computer readable program code toperform embodiments of the disclosure may be stored, in whole or inpart, temporarily or permanently, on a non-transitory computer readablemedium such as a CD, DVD, storage device, a diskette, a tape, flashmemory, physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that, when executed by a processor(s), isconfigured to perform one or more embodiments of the disclosure.

While the above figures show various configurations of components, otherconfigurations may be used without departing from the scope of thedisclosure. For example, various components may be combined to create asingle component. As another example, the functionality performed by asingle component may be performed by two or more components.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice, an event log comprising a plurality of events, wherein theplurality of events is derived from machine data generated by one ormore components of an information technology environment; determining afirst score associated with a first granularity level by comparing afirst event from the event log with a first plurality of frequentpatterns generated for the first granularity level, the first pluralityof frequent patterns determined from at least a first historic event inthe information technology environment; determining a second scoreassociated with a second granularity level by comparing the first eventwith a second plurality of frequent patterns generated for the secondgranularity level, the second plurality of frequent patterns determinedfrom at least a second historic event in the information technologyenvironment; determining an aggregate score for the first event based onthe first score and the second score; comparing the aggregate score forthe first event with an anomaly score threshold, the anomaly scorethreshold indicating when events in the information technologyenvironment are anomalous; and issuing an alert identifying the firstevent as an anomaly based on the aggregate score exceeding the anomalyscore threshold.
 2. The method of claim 1, wherein the first eventincludes a set of features, each feature of the set of featuresdetermined from one or more data items of the first event.
 3. The methodof claim 1, further comprising: identifying, within the first event, apattern of features corresponding to a frequent pattern of the firstplurality of frequent patterns; and identifying, within the first event,an unmatched feature of the first event.
 4. The method of claim 1,further comprising: determining a length of a pattern of features withinthe first event and a count of occurrences of the pattern of featureswithin the plurality of events; determining a contribution of thepattern of features to the first score based on the length and thecount; determining a penalty for an unmatched feature of the first eventbased on a cardinality of the plurality of events; and averaging thecontribution and the penalty, wherein the first score is based on aresult of the averaging.
 5. The method of claim 1, further comprising:determining a length of a pattern of features within the first event anda count of occurrences of the pattern of features within the pluralityof events; calculating a contribution of the pattern of features basedon the length and the count; calculating a penalty for an unmatchedfeature of the first event based on a cardinality of the plurality ofevents; applying a weight to the penalty in response to the unmatchedfeature existing on a black list; and calculating a weighted averagebased on the contribution, the penalty, and the weight.
 6. The method ofclaim 1, wherein determining the aggregate score for the first eventfurther comprises at least one of: multiplying the first score with thesecond score; or averaging at least the first score with the secondscore.
 7. The method of claim 1, further comprising: determining a thirdscore by comparing the first event with a third plurality of frequentpatterns generated for the second granularity level, wherein calculatingthe aggregate score for the first event comprises averaging the firstscore with a maximum selected from the second score and the third score.8. The method of claim 1, further comprising: determining an aggregatescore for a second event based on comparing the second event with thefirst plurality of frequent patterns and the second plurality offrequent patterns; comparing the aggregate score for the second eventwith the anomaly score threshold; and updating, in response to theaggregate score for the second event being less than the anomaly scorethreshold, the first plurality of frequent patterns based on the secondevent.
 9. The method of claim 1, further comprising: determining, inresponse to the aggregate score for the first event exceeding theanomaly score threshold, a count of anomalous events having a same setof features as the first event during a time window; and comparing thecount with a maximum count, wherein issuing the alert identifying thefirst event is further based on the count being less than the maximumcount.
 10. The method of claim 1, further comprising: determining asecond aggregate score for a second event based on comparing the secondevent with the first plurality of frequent patterns and the secondplurality of frequent patterns; comparing the second aggregate scorewith the anomaly score threshold; determining, in response to the secondaggregate score exceeding the anomaly score threshold, a count ofanomalous events having a same set of features as the second eventduring a time window; comparing the count with a maximum count; reducingthe aggregate score for the second event below the anomaly scorethreshold in response to the count exceeding the maximum count; andupdating, in response to reducing the aggregate score for the secondevent, at least the first plurality of frequent patterns based on thesecond event.
 11. The method of claim 1, further comprising: determininga second aggregate score for a second event based on comparing thesecond event with the first plurality of frequent patterns and thesecond plurality of frequent patterns; comparing the second aggregatescore with the anomaly score threshold; determining, in response to theaggregate score for the second event exceeding the anomaly scorethreshold, a count of anomalous events having a same set of features asthe second event during a time window; comparing the count with amaximum count; and reporting the second event in response to the countexceeding the maximum count.
 12. The method of claim 1, furthercomprising: determining a second aggregate score for a second eventbased on comparing the second event with the first plurality of frequentpatterns and the second plurality of frequent patterns, wherein thesecond aggregate score exceeds the anomaly score threshold; andgenerating a new frequent pattern by executing frequent itemset miningon at least the first event and the second event.
 13. The method ofclaim 1, further comprising: identifying a first feature associated withthe first event and a second feature associated with the first event,wherein the first feature is present in the first event and the secondfeature is not present in the first event; generating a first binaryencoding for the first event based on the first feature and the secondfeature; generating a second binary encoding for a second event; anddetermining the first event belongs to a first activity type and thesecond event belongs to a second activity type by executing a clusteringalgorithm on the first binary encoding and the second binary encoding,wherein the first granularity level and the second granularity levelcorrespond to the first activity type.
 14. The method of claim 1,further comprising: obtaining a plurality of historic events associatedwith the first granularity level; determining a minimum support valuebased on the plurality of historic events; and generating the firstplurality of frequent patterns by executing frequent itemset mining onthe plurality of historic events using the minimum support value. 15.The method of claim 1, further comprising: obtaining a plurality ofhistoric events; generating data points by executing frequent itemsetmining on different subsets of the plurality of historic events usingdifferent minimum support values, wherein the different subsets havedifferent cardinalities; determining, using the data points, a formulato calculate minimum support; obtaining a ratio of a desired number offrequent patterns to a cardinality of the plurality of historic events;determining, using the formula, a minimum support value based on theratio and the cardinality of the plurality of historic events, whereinthe ratio and the cardinality of the plurality of historic events areinputs to the formula; and generating the first plurality of frequentpatterns by executing frequent itemset mining on the plurality ofhistoric events using the minimum support value.
 16. The method of claim1, wherein the first historic event associated with first granularitylevel is the same as the second historic event associated with thesecond granularity level.
 17. The method of claim 1, wherein the firsthistoric event associated with the first granularity level is differentthan the second historic event associated with the second granularitylevel.
 18. A system comprising: memory comprising instructions; and acomputer processor for executing the instructions that cause thecomputer processor to perform operations comprising: receiving an eventlog comprising a plurality of events, wherein the plurality of events isderived from machine data generated by one or more components of aninformation technology environment; determining a first score associatedwith a first granularity level by comparing a first event from the eventlog with a first plurality of frequent patterns generated for the firstgranularity level, the first plurality of frequent patterns determinedfrom at least a first historic event in the information technologyenvironment; determining a second score associated with a secondgranularity level by comparing the first event with a second pluralityof frequent patterns generated for the second granularity level, thesecond plurality of frequent patterns determined from at least a secondhistoric event in the information technology environment; determining anaggregate score for the first event based on the first score and thesecond score; comparing the aggregate score for the first event with ananomaly score threshold, the anomaly score threshold indicating whenevents in the information technology environment are anomalous; andissuing an alert identifying the first event as an anomaly based on theaggregate score exceeding the anomaly score threshold.
 19. The system ofclaim 18, wherein the operations further comprise: identifying, withinthe first event, a pattern of features corresponding to a frequentpattern of the first plurality of frequent patterns; identifying, withinthe first event, an unmatched feature; determining a length of thepattern of features and a count of occurrences of the pattern offeatures within the plurality of events; determining a contribution ofthe pattern of features to the first score based on the length and thecount; determining a penalty for the unmatched feature based on acardinality of the plurality of events; applying a weight to the penaltyin response to the unmatched feature existing on a black list; anddetermining a weighted average based on the contribution, the penalty,and the weight.
 20. The system of claim 18, the operations furthercomprising: determining a second aggregate score for a second eventbased on comparing the second event with the first plurality of frequentpatterns and the second plurality of frequent patterns; comparing thesecond aggregate score with the anomaly score threshold; and updating,in response to the second aggregate score being less than the anomalyscore threshold, at least the first plurality of frequent patterns basedon the second event.
 21. The system of claim 18, the operations furthercomprising: determining, in response to the aggregate score exceedingthe anomaly score threshold, a count of anomalous events having a sameset of features as the first event during a time window; and comparingthe count with a maximum count, wherein issuing the alert identifyingthe first event is further based on the count being less than themaximum count.
 22. The system of claim 18, the operations furthercomprising: determining a second aggregate score for a second eventbased on comparing the second event with the first plurality of frequentpatterns and the second plurality of frequent patterns; comparing thesecond aggregate score with the anomaly score threshold; determining, inresponse to the second aggregate score exceeding the anomaly scorethreshold, a count of anomalous events having a same set of features asthe second event during a time window; comparing the count with amaximum count; reducing the second aggregate score below the anomalyscore threshold in response to the count exceeding the maximum count;and updating, in response to reducing the aggregate score for the secondevent, at least the first plurality of frequent patterns based on thesecond event.
 23. The system of claim 18, the operations furthercomprising: identifying a first feature associated with the first eventand a second feature associated with the first event, wherein the firstfeature is present in the first event and the second feature is notpresent in the first event; generating a first binary encoding for thefirst event based on the first feature and the second feature;generating a second binary encoding for a second event; and determiningthe first event belongs to a first activity type and the second eventbelongs to a second activity type by executing a clustering algorithm onat least the first binary encoding and the second binary encoding,wherein the first granularity level and the second granularity levelcorrespond to the first activity type.
 24. The system of claim 18, theoperations further comprising: obtaining a plurality of historic events;generating data points by executing frequent itemset mining on differentsubsets of the plurality of historic events using different minimumsupport values, wherein the different subsets have differentcardinalities; determining, using the data points, a formula tocalculate minimum support; obtaining a ratio of a desired number offrequent patterns to a cardinality of the plurality of historic events;determining, using the formula, a minimum support value based on theratio and the cardinality of the plurality of historic events, whereinthe ratio and the cardinality of the plurality of historic events areinputs to the formula; and generating the first plurality of frequentpatterns by executing frequent itemset mining on the plurality ofhistoric events using the minimum support value.
 25. A non-transitorycomputer-readable storage medium storing computer-readable program codewhich, when executed by one or more processors, cause the one or moreprocessors to perform operations comprising: receiving an event logcomprising a plurality of events, wherein the plurality of events isderived from machine data generated by one or more components of aninformation technology environment; determining a first score associatedwith a first granularity level by comparing a first event from the eventlog with a first plurality of frequent patterns generated for the firstgranularity level, the first plurality of frequent patterns determinedfrom at least a first historic event in the information technologyenvironment; determining a second score associated with a secondgranularity level by comparing the first event with a second pluralityof frequent patterns generated for the second granularity level, thesecond plurality of frequent patterns determined from at least a secondhistoric event in the information technology environment; determining anaggregate score for the first event based on the first score and thesecond score; comparing the aggregate score for the first event with ananomaly score threshold, the anomaly score threshold indicating whenevents in the information technology environment are anomalous; andissuing an alert identifying the first event as an anomaly based on theaggregate score exceeding the anomaly score threshold.
 26. Thenon-transitory computer-readable storage medium of claim 25, whereingenerating the first score comprises: identifying, within the firstevent, a pattern of features corresponding to a frequent pattern of thefirst plurality of frequent patterns; identifying, within the firstevent, an unmatched feature; determining a length of the pattern offeatures and a count of occurrences of the pattern of features withinthe plurality of events; determining a contribution of the pattern offeatures to the first score based on the length and the count;determining a penalty for the unmatched feature based on a cardinalityof the plurality of events; applying a weight to the penalty in responseto the unmatched feature existing on a black list; and determining aweighted average based on the contribution, the penalty, and the weight.27. The non-transitory computer-readable storage medium of claim 25, theoperations further comprising: determining a third score by comparingthe first event with a third plurality of frequent patterns associatedwith the second granularity level, wherein calculating the aggregatescore for the first event comprises averaging the first score with amaximum selected from the second score and the third score.
 28. Thenon-transitory computer-readable storage medium of claim 25, theoperations further comprising: determining a second aggregate score fora second event based on comparing the second event with the firstplurality of frequent patterns and the second plurality of frequentpatterns; comparing the second aggregate score with the anomaly scorethreshold; and updating, in response to the second aggregate score beingless than the anomaly score threshold, at least the first plurality offrequent patterns based on the second event.
 29. The non-transitorycomputer-readable storage medium of claim 25, the operations furthercomprising: determining a second aggregate score for a second eventbased on comparing the second event with the first plurality of frequentpatterns and the second plurality of frequent patterns; comparing thesecond aggregate score with the anomaly score threshold; determining, inresponse to the aggregate score for the second event exceeding theanomaly score threshold, a count of anomalous events having a same setof features as the second event during a time window; comparing thecount with a maximum count; reducing the aggregate score for the secondevent below the anomaly score threshold in response to the countexceeding the maximum count; and updating, in response to reducing theaggregate score for the second event, at least the first plurality offrequent patterns based on the second event.
 30. The non-transitorycomputer-readable storage medium of claim 25, the operations furthercomprising: obtaining a plurality of historic events; generating datapoints by executing frequent itemset mining on different subsets of theplurality of historic events using different minimum support values,wherein the different subsets have different cardinalities; determining,using the data points, a formula to calculate minimum support; obtaininga ratio of a desired number of frequent patterns to the cardinality ofthe plurality of historic events; determining, using the formula, aminimum support value based on the ratio and the cardinality of theplurality of historic events, wherein the ratio and the cardinality ofthe plurality of historic events are inputs to the formula; andgenerating the first plurality of frequent patterns by executingfrequent itemset mining on the plurality of historic events using theminimum support value.